Joint Communication Resource Allocation and Velocity Optimization in Advanced Air Mobility via Multi-Agent Reinforcement Learning

In response to traffic congestion in urban areas, the National Aeronautics and Space Administration promotes the concept of Advanced Air Mobility (AAM), which envisages a safe and efficient air transportation system. However, the increased communication demands in AAM can exacerbate the spectrum scarcity issue, so a new communication paradigm is necessary. In this paper, we consider multiple aerial vehicles (AV) flying along their shortest paths for cargo/passenger delivery. During the flight, each AV must make decisions in every time slot on communication resource allocation and velocity selection under safety constraints. Accordingly, we formulate the joint optimization problem to minimize the weighted sum of the total travel time and communication outage time. The optimization problem is formulated as a Markov game and a multi-agent reinforcement learning based algorithm is proposed. Simulation results corroborate the effectiveness of the proposed solution.